CN102707621A - Transparent models for large scale optimization and control - Google Patents
Transparent models for large scale optimization and control Download PDFInfo
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- CN102707621A CN102707621A CN2012100717566A CN201210071756A CN102707621A CN 102707621 A CN102707621 A CN 102707621A CN 2012100717566 A CN2012100717566 A CN 2012100717566A CN 201210071756 A CN201210071756 A CN 201210071756A CN 102707621 A CN102707621 A CN 102707621A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control/optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g., inputs, outputs, parameters, and so forth, of the parametric hybrid models), depending on the access level of the user.
Description
Technical field
Present invention relates in general to be used for the optimization of industrial plant and enterprise and the modeling of control.More specifically; The present invention relates to be used for graphics mode to the parameter mixture model carry out modeling, display parameter mixture model and with the mutual system and method for parameter mixture model, this parameter mixture model is used for optimizing and controls the operation of industrial plant and enterprise and/or the operation of some parts in their parts.
Background technology
Industrial plants (such as, manufacturing plant, refinery, generating plant, or even the campus in public factory) in the present situation of operation will plan, dispatch and control the standard that (manual work or automatic) conduct separates and treat.Particularly, the off-line that is regarded as with online (that is the input of parts during operation), (that is, operator or control system action) of accomplishing factory (that is, not in operating period of factory) activity will be planned and dispatched to current practice.
This branch meeting is introduced the remarkable challenge to robustness, cost benefit and the environment footprint (environmental footprint) of operation, and the operating personnel of factory and supvr and enterprise administrator early have recognized that this point.Yet the solution of this challenge has proved it is unintelligible.Although have, create " current " financial objectives that satisfies enterprise and remain difficult challenge in accordance with the production schedule of factory's operational constraints in sensing and the remarkable input of control aspect infrastructure, Database Systems and the business management software.
Factory's operation is often provided the infeasible or not optimum schedule of considering the current operating conditions of factory.When in the face of this challenge, because the model that will be used to dispatch is regarded as the black box to factory's operation, so factory's operation can't help the modification of out-of-date schedule.Model is usually directed to whole factory, the Knowledge Base of model thereby spread all over factory and distribute, thus added complicacy.According to the distributed group of the special knowledge that spreads all over factory consistent modification of correlation model proved remarkable challenge.In addition, scheduling problem setting and execution also are the not widely available cumbersome process of needed special knowledge.
Description of drawings
When describing in detail below with reference to advantages, of the present invention these with the understanding that will improve of further feature, aspect and advantage, in the accompanying drawings, similar Reference numeral is represented similar parts, wherein:
Fig. 1 is the synoptic diagram of exemplary commercial or industrial energy system;
Fig. 2 is the block diagram that illustrates the example components of energy system various interconnection, Fig. 1;
Fig. 3 is the block diagram that is used for the energy system of Fig. 1 is carried out the exemplary parameter mixture model of modeling;
Fig. 4 is the block diagram of the exemplary sweat cooling device piece of Fig. 2;
Fig. 5 is the block diagram of the exemplary boiler piece of Fig. 2;
Fig. 6 is the example of graphical user interface (that is diagrammatic representation) of the graphical modeling instrument of the expression a plurality of parameter mixture models relevant with the parts of the system of the Fig. 1 that is arranged to network;
Fig. 7 is used for the block diagram that parameter mixture model that the enterprise of the system of control chart 1 integrates is realized control system;
Fig. 8 is the example of the graphical user interface (that is diagrammatic representation) that illustrates the graphical modeling instrument in the component block storehouse that can use the user;
Fig. 9 is the example of the graphical user interface (, diagrammatic representation) of the graphical modeling instrument of the optimization view when illustrating the user and selecting to optimize label;
Figure 10 is the example of the graphical user interface (, diagrammatic representation) of the graphical modeling instrument of the optimization view when illustrating the user and having submitted the order input to and upgraded the optimal solution of system of Fig. 1;
Figure 11 is the protruding examples of approaching of non-linear and non-protruding optimization problem and this problem two;
Figure 12 is to use the example of solution figure of the optimization solving equation of parameter mixture model; And
Figure 13 is the example that is used to use the mutual method of graphical user interface and parameter mixture model.
Embodiment
As stated, between totally online the operating period of factory (that is, the) control of overall off-line (that is not during operation) plan of factory and schedule activities and factory and operant activity, usually there is uncontinuity.The embodiment that describes among this paper solves persistent three main challenges of facilitating this defective.At first, the embodiment that describes among this paper provides a kind of general modeling framework of the whole enterprise that is used to represent whole factory and in fact comprise one or more factory.Existing modeling framework is common: the correlative detail that (a) can't catch factory's operation because it is relevant with the economic goal of enterprise; Can't avoid the complicacy of forbidding under the situation of the number of the parts that (b) in the model of given expression factory, comprise, and (c) can't keep modularity so that between the parts of physics factory/process and model assembly, exist directly perceived corresponding.The embodiment that describes among this paper solves these challenges through adopting such as disclosed parameter hybrid modeling framework in No. the 10/842nd, 147, the U.S. Patent application, and the full content of this U.S. Patent application is incorporated this paper by reference into.
Secondly, the embodiment that describes among this paper solve with the off-line of the model of expression factory mutual (for example, modelling, plan, scheduling are mutual) and with the tradition of the online interaction (for example, control and operating interactive) of model separately.Particularly, in legacy system, the model of deployment is not to all user transparents.In other words, because mold portion is deployed to online environment, so the quality of measurement easily or Access Model and their parts.In these legacy systems, normally off-line utilization of the modification of model, the special knowledge that is used to revise model is high concentration normally.Yet, in fact, the people of parts of qualified modification model maybe be of no use with the qualification of another parts of revising model, these different people usually physically are being in the different location.In general, the asynchronous modification of model assembly is impossible, revises frequency and changes bigger according to types of models, operation scenario etc.The embodiment that describes among this paper solves these challenges through adopting the transparent model deployment strategy.
Once more, the embodiment that describes among this paper provides the graphics-optimized language of the communication barrier between a kind of elimination Optimization Software and the terminal user (plant operator, accounting department, Finance Department etc.).Particularly, the graphic language that is used to optimize makes more low-level competency can implement and/or dispose optimal solution.In other words, be not to have the doctor who optimizes background, the plant manager with procedural knowledge can have optimal solution.In addition, graphic language provides distributed development, deployment and the ability of maintenance, so that can be optimized problem and synthetic to the subsequent modification of optimization problem through the relevant input of stakeholder in their normal running is provided with.
The processing of the various aspects that the embodiment that describes among this paper makes it possible to operate according to the system mode of the complete transparency with target to the bottom model, priority and constraint (for example, the adjusting of the scheduled maintenance of critical component, about the energy efficiency of the robustness of the interruption in active volume or the supply chain, operation and low environment footprint, to the response of market price pressure etc.).Particularly; The embodiment that describes among this paper makes it possible to carry out on a large scale the figure of (potentially, non-linear) optimization problem according to the mode of optimal solution that can be through direct a factory simultaneously in order to the distributed collection guarder, that need not concentrate the stakeholder of power as information and affairs scope and/or enterprise-wide and sets, carries out and report.In order to obtain this target, the embodiment that describes among this paper comprises that core enables algorithm notion and software implementation method.
As stated, the embodiment that describes among this paper has many potential application scenarioss.For example, the embodiment that describes among this paper helps the improved plan and the scheduling of the operation in the industrial plant.Such as (for example to complicated energy user; Petrochemical industry association, campus, large-scale dwelling house association etc.) provide the complicated applications of steam, refrigeration power and water to relate to the lasting judgement that the operating personnel of factory carry out; Such as; Should use which resource, resource should be set what point (for example, capacity), resource are set how long should operate, should avoid what existing or imminent constraint etc.The complicacy of the judgement of in this application, making has confirmed the needs to the system optimization solution, but above-mentioned challenge has hindered the exploitation of global function solution up to now.
In addition, the embodiment that describes among this paper also helps the multiple-unit optimization in the industrial plant.Complex process in the scope of the boiler attendance in milk powder from milk plant oven dry to the generating plant is the multiple-unit operation inherently, its can from order to the energy efficiency, the cost that reduces the response that process is disturbed that improve for example operation, the optimized strategy that improves the ability that changes in order to profit ground response market condition etc. benefits.
In addition, the embodiment that describes among this paper helps the given optimization of accepting to fill a prescription for the product complex under the situation of choosing.Many manufacturing operations relate to production can be via the finished product that reaches for apolegamy side's (cheese of for example, in the milk plant, making).The embodiment that describes among this paper comprises the principle and method of the optimal scheduling of manufacture process, so that at any given time, makes the finished product with predetermined quality standard through the optimal set of batching.
The embodiment that describes among this paper also helps the purchase of optimizing the industrial plant on the electrical network and/or sells and judge.Electricity permitted great consumer (as, industrial plant or campus) have an inner generating capacity.Inner generating is just becoming more and complicated with respect to the economy of purchasing from electrical network, and this is because public company has abandoned fixed pricing so that their profit maximization.Each node on the electrical network current trend in the intelligent grid of source (that is the supplier of electric power) and this two execution of heating radiator (that is the consumer of electric power) of can be used as further makes to be judged and makes the process complicacy.The optimized solution can assist this consumer under given their situation of priority and target, to make best judgement at any given time.
The embodiment that describes among this paper comprises several aspects that make it possible to carry out above-mentioned application.For example, the embodiment that describes among this paper provides the online transparency to model quality and performance.Under the situation of the ability with investigation model quality (each unit and use network that these unit set up) of no use, can't keep the model fidelity.For example, utilize pure experience modeling example, cannot find out the source of quality degradation, so can not obtain the online observability of model fully.Detailed model based on first principle can suffer the transparency this lack.In addition, expect very much in order to the ability of under the situation that does not force model to lose efficacy, revising the target parts of deployment model.The online modification of the transparent model among this embodiment comprises and is not limited to the online modification of parameter adjustment, and the intension that comprises the new argument model is to replace the parameterized model of performing poor previously.Therefore, the online transparency of describing among this paper has been improved model quality and performance usually.
In addition, the embodiment that describes among this paper provides the asynchronous invention of problem formulation through distributed user's group.Extensive and the plant operator and the slip-stick artist's of optimization problem the responsibility and the limited range of competency make that the distributed asynchronous creation of problem narration is (with usually necessary) of expectation.For example, in public factory, chilled water loop and steam loop are coupled with mode of operation.The expert who understands the chilled water loop knows seldom for the steam line loop usually, and most possibly is not allowed to and/or does not want to bear the responsibility of steam line loop, and vice versa.Distributed invention also should be applied to the result of optimal solution.The result of the optimal solution of factory and/or enterprise-wide (for example, the Gantt chart of the operation schedule of the refrigerator of public factory) can appear user's's (for example, operator, plant manager etc.) distributed collection.In addition, make the stakeholder who authorizes not produce the schedule that editor proposes under the inconsistent situation.In addition, make distributed user to upgrade operational constraints and request reschedules with consistent mode.
The embodiment that describes among this paper also provides the figure invention of distributed user's group to problem formulation.Under the situation that does not have the graphics edition ability, common plant operator will can directly not help model maintenance.In addition, be not used in the definition optimization problem or explaining that common plant operator or slip-stick artist will can not help the meaningful definition of optimization problem under the situation of the graphic language that solver is judged.The figure invention of describing among this paper also is applied to the result of global optimization problem.The result of the optimal solution of factory and/or enterprise-wide (for example, the Gantt chart of the operation schedule of refrigerator) can appear user's's (for example, operator, plant manager etc.) distributed collection.The stakeholder who authorizes can not produce the schedule that proposes with the graphics mode editor under the inconsistent situation.In addition, distributed user's group can reschedule with graphics mode renewal operational constraints and request through consistent mode.The intuitive of figure invention has strengthened the availability and the uptime of optimal solution.
In addition, the embodiment that describes among this paper will merge from the real-time measurement and the information of fabrication facility floor and/or business system.In the optimization of factory and/or enterprise-wide, network usually comprises dynamic set, constraint and the target of a large amount of partial models, complex network connectivity, operating conditions.From spreading all over that enterprise distributes and usually being the information that source with function of local independence obtains to keep these " problem formulation " up-to-date needs.The solution that needs concentrated message to handle can become and have not a leg to stand on.Particularly, measure the model (for example, efficiency curve usually changes based on the current operating conditions of equipment) that influences in the problem formulation in real time.In order to obtain with measuring in real time the ability of integrating can be the obstacle that the optimal solution to factory successfully adopts.The model transparency changes the successful combination that helps real-time information owing to all relevant stakeholder can check.
Forward accompanying drawing now to, Fig. 1 is the synoptic diagram of exemplary commercial or industrial energy system 10.As described in more detail below, the energy system 10 of Fig. 1 is examples of factory's type of benefiting of the graphical modeling framework that can from this paper, describe.Fig. 1 illustrates typical various energy generations and consumable part in the commercial and industrial energy system.For example, Fig. 1 comprises boiler 12, and it is configured to receive fuel and generates the steam as the power source in other parts of energy system 10.For example; In certain embodiments; Wasted energy generator unit (cogeneration unit) 14 can use the steam that is produced by boiler 12 to drive generator 16, and generator 16 generates can be by the component consuming of energy system 10 and/or the electric energy that can sell to electrical network 18.In addition, in certain embodiments, heat recovery steam generates the secondary recovery that (HRSG) system 20 can be used for heat through the generation of steam, and it also can be used to drive generator 16 to generate electric energy.Except going out the sale of electricity to electrical network 18, energy system 10 can also be bought from electrical network 18.Energy system 10 any particular point in time from electrical network 18 buy still to electrical network 18 sell that the current electricity that depends on energy system 10 is supplied with, the electric storage volume of the current electric demand of energy system 10, energy system 10, from electrical network 18 buy/price of selling to electrical network 18, the cycle at day/night of energy system 10, be connected to availability and the capacity etc. of other electricity generation system of electrical network 18.
As shown, energy system 10 can comprise in consumed power, chilled water and/or the steam process unit 22 and the buildings 24 of some.In addition; In certain embodiments; Energy system 10 can comprise the electric refrigerator 26 and steam-refrigerated device 28 that can be associated with heat storage pool 30; And can consumed energy to generate chilled water, chilled water can be drawn into process unit 22 through pump 32 and be used for cooling with buildings 24, such as being used for buildings cooling, industrial process cooling etc.In addition, can make water cycle process cooling tower 34 and heat exchanger 36 that is associated and pump 38 from the for example heating of refrigerator 26,28, wherein the water cooling with heating is used for using after a while.
Therefore, generally speaking, various parts can be in typical commerce or industrial energy system 10 produce power (that is, being called the source) and/or consumed energy (that is, being called heating radiator (sink)).In fact, the parts shown in Fig. 1 only are exemplary to the parts that can comprise typical commerce or industrial energy system 10.As shown in Figure 1, the various parts of energy system 10 can be configured to based on different technologies consumption and/or produce power.In certain embodiments, the interdependence meeting of the parts of energy system 10 is extremely complicated.In addition, various external components (such as, the electrical network 18) complicacy that can add energy system 10.Moreover it only is exemplary that 10 pairs of the energy systems shown in Fig. 1 can use the complex plant of the graphical modeling framework of describing among this paper and the type of enterprise.
Fig. 2 is the block diagram that illustrates the example components of energy system 10 various interconnection, Fig. 1.Particularly, Fig. 2 has described typical various energy loops in the commercial and industrial energy system 10.For example, crucial energy loop comprises fuel loop 40, electric loop 42, condenser loop 44 (for example, cooling tower water), evaporator loop 46 (for example, refrigerator water) and steam loop 48.Various energy loops 40,42,44,46,48 shown in Fig. 2 only are exemplary and are not to be intended to restriction.In other embodiments, can use other energy loop to come energy system 10 is carried out modeling.
Each energy loop 40,42,44,46,48 comprises one group of defining variable as the input and output of each energy loop 40,42,44,46,48.For example, fuel loop 40 comprises t
G, p
G, f
GAnd r, wherein, t
GBe fuel temperature, p
GBe fuel pressure, f
GBe fuel flow rate, r is the hot factor of fuel loop 40.Electricity loop 42 comprises the kw as the electric weight that provides.Condenser loop 44 comprises ts
C, tf
CAnd f
C, wherein, ts
CBe the temperature that gets into the water of cooling tower, tf
CBe temperature from the water of cooling tower discharge, f
CIt is the flow rate of water in the condenser loop 44.Evaporator loop 46 comprises ts
E, tf
EAnd f
E, wherein, ts
EBe the temperature of leaving the chilled water of refrigerator, tf
EBe the temperature of getting back to the chilled water of refrigerator, f
EIt is the chilled water flow rate.Steam loop 48 comprises t
S, p
SAnd f
S, wherein, t
SBe vapor (steam) temperature, p
SBe vapor pressure, f
SIt is vapor stream.Moreover all variablees in the variable of the energy loop 40,42,44,46,48 shown in Fig. 2 only are exemplary and are not to be intended to restriction.In other embodiments, can use other variable-definition energy loop 40,42,44,46,48.
As shown, energy loop 40,42,44,46,48 is coupled to following component block: its expression usually provides energy or consumes the actual energy relevant device group from the energy system 10 of the energy of energy loop 40,42,44,46,48 to energy loop 40,42,44,46,48.For example; Boiler piece 50 be coupled to fuel loop 40 and steam loop 48 the two; Generator piece 52 is coupled to fuel loop 40, electric loop 42 and steam loop 48; Sweat cooling device piece 54 is coupled to electric loop 42, condenser loop 44 and evaporator loop 46, and absorption refrigeration device piece 56 is coupled to evaporator loop 46 and steam loop 48.Moreover each component block 50,52,54,56 shown in Fig. 2 only is exemplary and is not to be intended to restriction.In other embodiments, other component block can be coupled to each energy loop 40,42,44,46,48.
Disclosed embodiment helps the plan/scheduling and the control/operation of the energy system 10 of Fig. 1 and Fig. 2.More specifically; As described in more detail below; The embodiment that describes among this paper comprise make special knowledge with extensive different field distributed user different sets can with graphic language and the interface and the transparent modeling framework of the energy system 10 of the mutual Fig. 1 of the parameter mixture model of each component block (for example, equipment group) of energy system 10 and Fig. 2.In fact; Be to be understood that; Though the embodiment that describes among this paper is rendered as relevant with the energy efficient operation of energy system 10; But in other embodiments, can the graphic language of the embodiment that describe among this paper and interface and transparent modeling framework be expanded to other application, such as chemistry manufacturing, oil and gas disposal etc.
The purpose of disclosed embodiment is to be optimized having solved Fig. 1 and the energy system 10 of Fig. 2 that many different-energy associated components to energy system 10 carry out the computational complexity challenge of modeling (comprise each parameter mixture model that is used for generator unit, boiler, refrigerator, pump and fan etc. and be used to retrain and the parameter mixture model of target).In addition, disclosed embodiment provides the model structure of carrying out through the different sets of distributed user and/or the online modification of parameter via graphic language and interface and transparent modeling framework.
Can set up the economic goal of parameter objectives function with the operation of reflection energy system 10.Can set up restriction on the parameters set with the constraint of the operation of reflection energy system 10 (for example, to the constraint of cooling capacity, to the constraint that allows to discharge etc.).As described in more detail below; Even the visit to bottom parameter mixture model (for example is limited to the specific user; The modeling expert), the graphic language of describing among this paper makes that also all stakeholder in the energy system 10 can be mutual with the parameter of parameter mixture model, parameter objectives function and restriction on the parameters set.Can also set up the energy charge model with the load profile in the predicted operation time range.Load model can comprise for example chilled water demand, steam demand, electric demand etc.Based on all these models and target, the optimization problem that can solve energy system 10 subsequently is with the optimum profiles of the operating conditions of the energy system 10 of confirming to submit to the restriction on the parameters set.
Because the complicacy of typical commercial and industrial energy system 10, the hybrid technology of describing among this paper provides unique advantage.Hybrid technology is to because the shortage of basic comprehension and not by the phenomenon of accurate modeling, utilizes the known fundamental relation that (leverage) more or less can get from the basic process modeling that utilizes the experience modeling technique (for example, known kinetic energy model etc.).Because design and develop the industrial scale energy device uniquely to intensive action usually, so the experience modeling technique that utilizes concrete design is to announcing or the remarkable calibration or the adjusting of available basic modeling provide energy model more accurately.Then, energy model makes it possible to realize based on Model Optimization of more highly carrying out and control solution more accurately.Therefore, the best available basic model and the empirical model merging of the energy device measurement/performance data optimum matching of gathering in the stage with the change operation of energy system 10 will regulated or be calibrated to desirable modeling solution.According to the accuracy of parameter mixture model, can discern and use linearity (for example, single value) parameter or non-linear the kinetic energy parameters of the energy changing of measuring (for example, with) variable.
Fig. 3 is the block diagram that is used for energy system 10 and/or more specifically carries out the exemplary parameter mixture model 58 of modeling for each component block 50,52,54,56 of energy system 10.As shown, parameter mixture model 58 can receive the energy variable input u from energy system 10
kEnergy variable input u
kThe variable that for example can comprise above-mentioned energy loop 40,42,44,46,48.Empirical model 60 can use energy variable input u
kGenerate empirical model output w
kEmpirical model output w
kCan be energy variable input u
kFunction with empirical model parameter ρ.Can empirical model be exported w
kWith energy variable input u
kThis two be directed in the parameter model 62 of parameter mixture model 58.Basic model parameter θ from parameter model 62
kCan be energy variable input u
kWith empirical model output w
kFunction.Should be noted that basic model parameter θ
kThe value this two of length and parameter vector can be used as energy variable input u
kWith empirical model output w
kFunction.In certain embodiments, basic model parameter θ
kCan comprise empirical model output w
k, perhaps can export w with empirical model simply with their the simplest forms
kIdentical.Can be with basic model parameter θ
kBeing directed to can be in parameter first principle model 64 of stable state or dynamic model.In addition, parameter first principle model 64 can receive the energy variable input u from energy system 10
kParameter first principle model 64 can be to measuring or unmeasured energy state variable x
kWith energy variable output y
kCarry out modeling.Energy state variable x
kCan be energy variable input u
k, previous energy state variable x
k, and basic model parameter θ
kFunction.Energy variable output y
kCan be energy variable input u
k, current energy state variable x
k, and basic model parameter θ
kFunction.Can be from parameter mixture model 58 guiding energy variablees output y
kAs output.Therefore, the common formula of defined parameters mixture model 58 comprises:
w
k＝f
1(u
k，ρ)；
θ
k＝f
2(u
k，w
k)；
x
k=F
k(u
k, x
K-1, θ
k); And
y
k＝G
k(u
k，x
k，θ
k)；
Wherein, u
kBe the vector that time k goes up the energy variable input, ρ is the vector of empirical model parameter, w
kBe the vector that time k goes up empirical model output, θ
kBe the vector that time k goes up the basic model parameter, x
kBe the vector that time k goes up measurement or unmeasured energy state variable, y
kIt is the vector that time k goes up energy variable output.
For example, can't measure the efficient of boiler in operating period of energy system 10, and the efficient of boiler can as with the energy variable input and output u of boiler component piece 50
k, y
kBe correlated with but parameter inequality.Therefore; Can utilize parameter mixture model 58 (more specifically at energy system 10; The parts of boiler component piece 50) operating period is calculated this parameter, and can in the process of the optimum trajectory that calculates the input of boiler (for example, the combustion rate of boiler), use this parameter.This allows the better control in real time of the operating period of energy system 10, so that can closer target and the intermediate performance of keeping energy system 10.In certain embodiments, can confirm the optimum trajectory function through finding the solution following equation:
w
k＝f(u
k，p)；
θ
k＝f(u
k，w
k)；
x
k＝F
k(u
k，x
k-1，θ
k)；
y
k=G
k(u
k, x
k, θ
k); And
L＜u
k＜H；
Wherein, Γ () is that the objective function of definition is gone up in energy variable output,
be that energy variable output
is the explicit or implicit representation of expectation energy variable track.In addition, constraint (for example, above L and H) can be a lopcus function.Through to decision variable u
kThe adjustment of (for example, energy variable input) obtains minimizing of above objective function.Notice that above optimization problem only is exemplary and is not to be intended to restriction.For example, can objective function Γ () be defined as and comprise decision variable u
kPenalize.
Can make ins all sorts of ways implements above-mentioned dynamic optimization.The rank of the details that comprises in the parameter mixture model 58 can change according to the rank of the complicacy that can handle in real time.In other words, the parameter hybrid modeling allows system mode compromise between model accuracy and computational complexity, and therefore the dirigibility that makes the energy system 10 that the rank of complicacy changes in order to processing is provided.More specifically, the complicacy of any given parameter mixture model 58 is by the complicacy of system for modeling and make the function that calculates the simplicity this two that is prone to handle parameters needed mixture model 58 in real time.So, parameter mixture model framework is provided for best the equilibrium model accuracy with respect to the system framework of operation efficiency.In the process of defined parameters mixture model 58, in certain embodiments, can use simple and direct model (for example, in parameter first principle model 64).These simple and direct models can be linear or nonlinear, dynamically or stable state etc.Parameter mixture model framework utilizes the true-time operation condition of energy system 10 to remain current; And to allow not be the online modification of the model parameter that directly inputs or outputs of energy system 10; So judging engine (for example, optimization and control) always has as the valid model of judging the basis.
As above said to Fig. 2, each component block 50,52,54,56 can be associated with the energy loop 40,42,44,46,48 of the operation that helps component block 50,52,54,56.In addition, each component block 50,52,54,56 will comprise actual energy relevant device parts.Moreover, can carry out modeling through parameter mixture model 58 to each component block 50,52,54,56 to Fig. 3 is said as above.For example, Fig. 4 is the block diagram of the exemplary sweat cooling device piece 54 of Fig. 2.As shown, sweat cooling device piece 54 can comprise condenser 66, compressor reducer 68, evaporator 70 and valve 72.So, sweat cooling device piece 54 can be associated with condenser loop 44 (for example, condenser 66), electric loop 42 (for example, compressor reducer 68) and evaporator loop 46 (for example, evaporator 70).
Correspondingly, the variable of condenser loop 44, electric loop 42 and evaporator loop 46 will be associated with sweat cooling device piece 54.More specifically, variable ts
C, tf
C, f
C, kw, ts
E, tf
E, and f
EThe input and output energy variable u that comprises sweat cooling device piece 54
k, y
kYet; Can set up following this parameter mixture model 58: the basic model of combination condenser 66, compressor reducer 68, evaporator 70 and valve 72 (for example; In parameter model 62); The empirical data (for example, in empirical model 60) relevant, and parameter first principle model 64 of sweat cooling device piece 54 with condenser 66, compressor reducer 68, evaporator 70 and valve 72.In view of the above, the parameter mixture model 58 of sweat cooling device piece 54 will be to the key parameter θ of sweat cooling device piece 54
kCarry out modeling.These key parameters θ
kInput and output energy variable u with sweat cooling device piece 54
k, y
kDifferent.Yet they are relevant with the performance standard of sweat cooling device piece 54.For example, the key parameter of sweat cooling device piece 54 can comprise entropy production and thermal impedance.The input and output energy variable u of these parameters and sweat cooling device piece 54
k, y
k(for example, ts
C, tf
C, f
C, kw, ts
E, tf
E, and f
E) good relevant, but and be not equal to said input and output energy variable u
k, y
k
As another instance, Fig. 5 is the block diagram of the exemplary boiler piece 50 of Fig. 2.Illustrative like institute, boiler piece 50 can comprise stove 74, energy-saving appliance 76 and steamdrum 78.Like this, boiler piece 50 can be associated with fuel loop 40 (for example, stove 74 and energy-saving appliance 76) and steam loop 48 (for example, steamdrum 78).Correspondingly, the variable of fuel loop 40 and steam loop 48 will be associated with boiler piece 50.More specifically, variable tG, pG, fG, r, t
S, p
SAnd f
SThe input and output energy variable u that comprises boiler piece 50
k, y
kYet; Can set up following this parameter mixture model 58: the basic model of merging stove 74, energy-saving appliance 76 and steamdrum 78 (for example; In parameter model 62); The empirical data (for example, in empirical model 60) relevant, and parameter first principle model 64 of boiler piece 50 with stove 74, energy-saving appliance 76 and steamdrum 78.In view of the above, the parameter mixture model 58 of boiler piece 50 can generate boiler piece 50 key parameter θ
kModel.These key parameters θ
kInput and output energy variable u with boiler piece 50
k, y
kDifferent.Yet they are relevant with the performance criteria of boiler piece 50.For example, the key parameter of boiler piece 50 can comprise the efficient of stove.The input and output energy variable u of this parameter and boiler piece 50
k, y
k(for example, tG, pG, fG, r, t
S, p
SAnd f
S) good relevant, but unequal.
Therefore, can set up parameter mixture model 58 for the various component blocks 50,52,54,56 of energy system 10.The parts of component block 50,52,54,56 can comprise generator unit, such as gas turbine, wind turbine, solar panels etc.As stated, electrical network 18 also can be used as energy source to be considered, and can carry out modeling by operation parameter mixture model 58.Other parts of component block 50,52,54,56 that can modeling comprise refrigerator (for example, such as example among Fig. 4), boiler (for example, such as example among Fig. 5), cooling tower, pump, fan, motor, thermmal storage unit etc.In addition, can be to load (such as steam load, through the water load, electric load etc. of refrigeration) development parameters mixture model 58.In addition, can be to various energy sources and other parameter mixture model 58 of power consumption parts exploitation.In addition, not only can be to component block 50,52,54,56 (such as example among Fig. 2) development parameters mixture model 58, but also can develop the parameter mixture model 58 of the interconnection (for example, the energy loop 40,42,44,46,48) between the parts.
The various embodiment of the system and method that is used for application parameter mixture model 58 are described below.In this approach, can be incorporated in the parameter mixture model 58 of definition energy system 10 in the system administration manager/controller based on the parameter mixture model as integrating model.In other content, this system can estimate or predicts energy system 10, what to take place based on the recent historical data of the prediction of integration parameters mixture model 58 and the weather/load that for example comprises operating conditions and/or state value recently and can obtain from a lot of sources that comprise other parameter mixture model 58 etc.Can should estimate or predict based on constraint, intended target and/or the renewal of reception current information or the biasing of energy system 10.Can use the optimized Algorithm estimation that the best current and following control of model input is adjusted to realize the Expected Response of energy system 10.Target is set, and can compares integration parameters mixture model output with exporting how to show, to keep the expectation accuracy of integration parameters mixture model 58.
As stated, can be to any component block development parameters mixture model 58 in the component block (for example, the component block 50,52,54,56 of above-mentioned energy system 10) of system.In addition, can be linked at parameter mixture model 58 together to form network with the mutual each other parameter mixture model 58 of factory or enterprise-wide mode.So, not only the complex operations of 58 pairs of system's 10 each component blocks of parameter mixture model is carried out modeling, and the mutual formation between each parameter mixture model 58 has the network of complex data stream and constraint between the parameter mixture model 58.
Can use relation and data stream between the graphical modeling instrument definition parameter mixture model 58.More specifically; The graphical modeling instrument can be configured to represent between the parts of system relation (for example; Product stream between spatial relationship between the parts, the stream of the fluid between the parts, the parts, the energy stream between the parts etc.); Wherein, 58 pairs of parts of being represented by the graphical modeling instrument of operation parameter mixture model carry out modeling.For example, Fig. 6 is the instance of graphical user interface 80 (that is diagrammatic representation) of the graphical modeling instrument 82 of expression a plurality of parameter mixture models 58 relevant with the parts of the system that is arranged to network 84 10.Especially, in the instance of example, system 10 comprises: as four refrigerator component blocks 88 (promptly; EC.0, EC.1, EC.2 and EC.3) power supply electrical network component block 86 (promptly; P.0), and (that is, CW.0) as the chilled water component block 90 of the heating radiator of four refrigerator component blocks 88.In the component block 86,88,90 each is carried out modeling as parameter mixture model 58 as stated; And being expressed as with graphics mode can be via the node 92 that also is connected to other node 92 (that is other component block 86,88,90) as the connection 94 of parameter mixture model modeling.
Each in the 94 relevant nodes 92 that is connected of definition and component block 86,88,90 and component block 86,88,90 makes that the exemplary network 84 among Fig. 6 clearly defines right fixed optimization problem.So, through the decision variable in the optimization problem and parameter characterization node 92 be connected in 94 each.Therefore, in the diagrammatic representation of optimization problem, how node 92 influences objective function if being caught decision variable.This along with the connection between node reflection in the simulating scenes as physical influence to the output of a node of the input of another node, and distinguish the diagrammatic representation of optimization problem (example in the network 84) and the diagrammatic representation that is commonly used to process is carried out emulation.Extract these input and output streams more generally of going to and coming node 92 automatic network 84 from decision variable fully.Therefore, each in the parts 94 comprises to directly the translating of the optimization problem that makes up and keep through graphic language (translate).This allows the connection 94 between modeling expert's development parameters mixture model 58 and the parameter mixture model 58, but permission system 10, anyly have the visit of graphical modeling instrument 82 and be authorized to check and/or the user of the parameter mixture model 58 that modification is relevant with graphics part can check illustrative graphics part among Fig. 6.
Fig. 7 is used to control and enterprise's integration parameters mixture model of optimizing the system 10 of Fig. 1 is realized the block diagram of control/optimization system 96.As described in more detail below, control/optimization system 96 comprises the graphical user interface 80 explicit graphical modeling instruments 82 of giving the user of control/optimization system 96 that make it possible to example among Fig. 6.More specifically, have to the user of the visit of control/optimization system 96 can be on any compatible electronic device display graphics user interface 80, with mutual with the parameter mixture model 58 of the parts of expression system 10.Routine as shown in Figure 7, control/optimization system 96 is directly connected to system 10.More specifically, in a particular embodiment, control system 96 can comprise a plurality of sensors 98 and actuator 100 of each parts 102 (that is physical equipment) of the system of being connected to 10.In general; Sensor 98 is configured to receive the relevant signal of operation information with the parts 102 of system 10; And actuator 100 is configured to receive the signal by control system 96 transmissions that is used for control assembly 102 operations (that is valve setting,, pump and compressor speed etc.).
So, control/optimization system 96 is the computer systems that are used for control system 10 operations.Control/optimization system 96 can comprise according to any content in the network of various types of computer systems of the various embodiment software program for execution of describing among this paper 104 or computer system.Software program 104 can executive system 10 the various aspects of modeling, prediction, optimization and/or control.Control/optimization system 96 can further be provided for use and optimize the environment that solver (solver) is made optimal decision and carried out these judgements (for example, with control system 10).Especially, the parameter mixture model control that control/optimization system 96 can implementation system 10.More specifically, can use the parameter mixture model control of the parameter mixture model 58 realization systems 10 relevant with the parts of system 10 102.
In addition, control/optimization system 96 is configured to generate and sends the graphical user interface of describing among Fig. 6 80 to the long-distance user 106 of control/optimization system 96.More specifically, control/optimization system 96 is configured on communication network 108 to send to the long-range electronic installation 110 of the system that can be positioned at 10 to graphical user interface 80.For example, in a particular embodiment, communication network 108 can comprise Local Area Network.Yet in order to generation with under the situation of the server that is positioned at electronic installation 110 transmission graphical user interface 80 Anywhere, communication network 108 can also comprise the internet in 96 conducts of control/optimization system.Electronic installation 110 can be a desktop computer; Laptop computer; Smart phone, perhaps can be on the display 112 of electronic installation 110 display graphics user interface 80 and any other electronic installation that can receive from electronic installation 110 users' 106 input via the interface 114 of electronic installation 110.Be designed to control/optimization system 96 to use so that parameter mixture model 58 is always merging to the potential asynchronous input from Local or Remote user 106 in line model after appropriate globality inspection.When these models of definition, be embedded in these globality inspections in the parameter mixture model 58.
Control/optimization system 96 comprises the non-provisional storage medium 116 of storing software program 104, the data relevant with parameter mixture model 58, service data (real-time and historical) of system 10 etc.Term " storage medium " is intended to comprise various types of storeies or storage; Comprise: medium is installed (for example; CD-ROM or floppy disk); Such as computer system memory or the RAS of DRAM, SRAM, EDO RAM, Rambus RAM etc., perhaps such as the nonvolatile memory of magnetic medium (for example, hard disk drive) or optical memory.Storage medium 116 also can comprise the storer of other type, perhaps its combination.Execution comprises that from the code of storage medium 116 and the processor 118 of data the method that is used for describing according to this paper is created and the parts of software program for execution 104.Control/optimization system 96 can take to comprise the various forms of personal computer system, large computer system, workstation, the network facilities, internet facilities or other device.Usually, can extensively be defined as and contain processor 118 (or processor) and carry out any device (or cluster of device) from the instruction of storage medium 116 (or medium) term " computer system ".
The user 106 of control/optimization system 96 can have and can in electronic installation 110, confirm during the typing login certificate user 106, perhaps can use the security access rank of the variation that other method (first-class such as making access rights be stored in electronic installation 110) confirms.For example, like institute's illustration among Fig. 7, the user 106 of control/optimization system 96 can comprise supvr's rank user 120 and slip-stick artist's rank user 122 (for example, factory engineering teacher or operator).As described in more detail below, supvr's rank user 120 can have the visit to the characteristic that only can use slip-stick artist's rank user 122 (for example, order input) subclass.For example; Can allow supvr's rank user 120 to revise the optimization constraint of parameter mixture model 58 of the parts 102 of expression systems 10, and can allow slip-stick artist's rank user 122 to revise the optimization constraint of parameter mixture models 58 and also revise bottom parameter mixture model 58.So, the order input that in the graphical user interface 80 that user 106 sends, realizes will change according to specific user 106 security access rank.
When user's 106 submiting commands inputs (for example, click node 92 or connect 94 with node 92 or be connected 94 mutual) time, will be basically in real time the operating period of system 10 (for example) other user 106 of order input notice control system 96.In other words, will send to control/optimization system 96 to the electronic installation 110 that the order input is just being used from user 106, and will push other electronic installation 110 that (that is broadcasting) is using for other user 106 to the effect of handle order input.So, with parameter mixture model 58 take place alternately will be transparent to all users 106 of control system 96.User 106 can also through change to be interpreted as to specific user 106 it is local and not mutual to sandbox (sand-box) pattern and the control/optimization system 96 of the influence of online application.If this sandbox mode allows each user 106 carry out-how about to analyze, for example, the current state of using system 10 under the situation of not disturbing online application.Though can local (for example, on electronic installation 110) if the scene of record emulation-how about in a particular embodiment, can be passed through licensing process to any submitting of the change of control/optimization system 96.For example, slip-stick artist's rank user 122 meetings need be permitted the IF-THEN scene before submitting them.
In addition, as supplying a plurality of each model of requested service device service deployment to a plurality of electronic installations 110.This makes that all users 106 can be in the running of the operating period of system 10 investigation parameter mixture model 58.More specifically, along with model is disposed and moved, each node 92 (for example, component block) relevant with the parts of system 10 102 can provide information to user 106 via graphical user interface 80.So, user 106 can check the data relevant with the accuracy of model in the operating period of system 10.In addition, the same model of disposing will provide other business, when control/optimization system 96 is just being used it, calculates the critical nature indicator such as being used for.
As stated, regard modelling verification as offline activity traditionally.Yet the algorithm of the logic of the built-in data filter of describing among this paper of embodiment and parameter recognition (for example, as separating of closing form) and optimization is as the attribute of deployment parameters mixture model 58, and the model of creation mass measurement is as the parameter of parameter mixture model 58.More specifically, again, graphical modeling instrument 82 is as server service, and what allow to dispose avoids performance degradation during in the computation model mass measurement at line model (that is the network 84 of parameter mixture model 58).Model quality information in a particular embodiment, is mapped to model parameter to model quality, so that can be used to the user 106 of control system 96.For example, operation parameter mixture model 58, model error can easily be associated with model parameter (for example, through the tolerance interval of defined parameters), and user 106 can take concrete action in response to the model quality deterioration.
The deployment strategy of transparency parameter mixture model 58 makes it possible to carry out the distributed and asynchronous checking and the modification of deployment model.This is useful especially because parts of model spread all over that factory and/or enterprise distribute.In addition, the transparency is two-way.In other words, under any authorized user 106 addressable situation of model quality to control system 96, any modification that any authorized user 106 carries out is transparent to all authorized users 106.In addition, the parameter person's character of model makes it possible to carry out the diagrammatic representation (for example, the border on the model parameter, wherein, the currency of parameter drops in the border etc.) of model quality.
Because transparency parameter mixture model 58 comprises the potential distributed elements 102 with the parts 102 corresponding owners and stakeholder, so guarantee the globality of deployment model through efficient entitlement modeling.For example, model entitlement (for example, concrete parameter mixture model 58 grades) is the build-in attribute of deployment model.The ownership property that uses concrete parameter mixture model 58 is as can authentication and the key of implementing 58 visits of parameter mixture model and revising.In other words, if user 106 is not the owner of special parameter mixture model 58,, then can stop user 106 and parameter mixture model 58 mutual perhaps not to the abundant access rights of parameter mixture model 58.80 of the graphical user interface of in other words, presenting to user 106 via electronic installation 110 present user 106 through the action (that is order input) that user 106 has visit.Ownership property be applied to factory and/or enterprise prototype network 84 node 92 be connected 94 this two; Therefore; Ownership property is used for the checking (for example, parameter mixture model 58 to increase and deletion from prototype network 84) of parameter mixture model 58 any graphical manipulations.
In addition, some graphical manipulation of the parameter mixture model 58 of certain user's 106 execution (that is order input) can be accepted the permission that other user 106 carries out before implementing.For example, in a particular embodiment, the order input that supvr's rank user 120 carries out can be accepted the permission that slip-stick artist's rank user 122 carries out before implementing.The transparent person's character of graphical modeling instrument 82 makes it possible to carry out this permission mechanism because of the order input of carrying out to the Any user 106 of graphical user interface 80 push control system 96 of other device 110 that is connected to control system 96 in real time basically.
For example, get back to Fig. 6 now, 106 needs of the user of graphical modeling instrument 82 are mutual via graphical user interface 80 and graphical information.For example, if user 106 hopes the constraint of increase or modification system 10, then 106 of users need click to bring the node 92 of the dialog box that makes user 106 can increase constraint information or connect 94.In addition, the user 106 of graphical modeling instrument 82 can increase and/or the deletion component block from graphical user interface 80.In other words, just need not to represent whole in each physical unit 102 of the real system 10 of modeling and optimization via graphical user interface 80 with the component block of any given network 84 expressions.But user 106 can only pay close attention to the specific collection of the physical unit 102 of (or visit) system 10.So, user 106 can personalized graphical user interface 80 to comprise the component block that user 106 is paid close attention to.
For example, Fig. 8 is the instance of graphical user interface 80 (that is diagrammatic representation) of graphical modeling instrument 82 in the storehouse 124 of the illustration component block that can use user 106 that will increase to graphical user interface 80.For example, user 106 can drag any component block in the component block of listing in the storehouse 124 and put in the graphical user interface 80.In a particular embodiment, graphical modeling instrument 82 will create automatically and/or remove graphical user interface 80 that user 106 just checks via user 106 increases and/or the component block (that is, node 92) of deletion between suitable connection 94.In addition, will be understood that, can preserve setting as required with the personalized graphical user interface 80 of opening user's 106 establishments again.
So, any special pattern of system 10 is represented and can be transmitted different information to user 106 according to relating to figured context.For example; If user 106 selects the model label 126 of graphical modeling instrument 82; And click four refrigerator component blocks 88 (promptly; EC.0, EC.1, EC.2 and EC.3) in one with chilled water component block 90 between be connected 94, then initiate dialog box, for example to show flow rate, temperature and the pressure leave refrigerator component block 88 through the water of refrigeration.Yet; If the user selects the web tab 128 (the supposition user has the visit to web tab 128) of graphical modeling instrument 82; And click and to be connected 94 between refrigerator component block 88 and the chilled water component block 90; Then initiate dialog box, with the water tonnage that for example shows that refrigerator component block 88 produces through refrigeration.
In other words, decision variable or the constraint (for example, parameter) of the parameter mixture model 58 of expression component block when selecting web tab 128 (, in the time of in network schemer) to user-accessible.Yet (, in the time of in network schemer) do not show the actual physics input and output of describing particular device when selecting web tab 128.But only (, in the time of in modeling or operator scheme) is shown to the user to the actual physics input and output of describing particular device when preference pattern label 126.So, in a particular embodiment, the user who gos deep into knowledge 106 (for example, slip-stick artist's rank user 122) who only has the parameter mixture model 58 of expression component block can have the visit to model label 126.Therefore, have only these users 106 will be mutual with the actual physics input and output of particular device.On the contrary, having of system 10 can be mutual so that executive system 10 is optimized and the purpose of control with the decision variable of system 10 constraints to the Any user 106 of the visit of web tab 128.
Each node 92 in the network 84 can represent that system 10 optimizes and the objective function of control.This can be useful especially under the situation that will control a plurality of Action Targets via graphical user interface 80 with graphics mode.The all types of target meeting can be mutual via graphical user interface 80, and so, user 106 can revise the optimization problem of system 10 through graphics mode.For example, in a particular embodiment, graphical modeling instrument 82 can for user 106 appear can the modify certain parameters mixture model 58 optimize the value of constraint scope.In other words, under the situation of the permission that does not need slip-stick artist's rank user 122 to carry out, graphical user interface 80 can allow the interior optimization constraint of bounds of the feasible value of supvr's rank user 120 modification systems 10 controls.
Any and all order inputs optimization aim of define system 10 again that user 106 submits to.For example; Can import through the order that user 106 submits to via graphical user interface 80; The refrigerator network of the water that optimization reception electric energy and generation warp freeze (for example; The network 84 of example among Fig. 6 and 8), uses minimum chilled water load, perhaps make the chilled water of given maximum available electrical energy produce maximization with produce power.For example, be elected to when changing label 130 according to qualifications, user 106 can be mutual with the optimization constraint of network 84.
For example, the instance of the graphical user interface 80 of the graphical modeling instrument 82 of the optimization view 132 of Fig. 9 when to be illustration by user 106 select to optimize labels 130 (, diagrammatic representation).More specifically, utilize the optimization label of selecting 130, when Fig. 9 illustration user 106 clicks chilled water component block 90.The time series 134 of the expectation chilled water demand of the optimization view 132 illustration chilled water component blocks of so, describing among Fig. 9 90.In addition, the optimization view 132 of chilled water component block 90 comprises in four refrigerator component blocks 88 that are connected to chilled water component block 90 the time schedule 136 of each.More specifically, when the time schedule has been described in the refrigerator component block 88 each is scheduling to the water demand of operation with the expectation refrigeration that realizes chilled water component block 90.
Suppose that authorized user 106 and chilled water component block 90 are mutual, then user 106 can revise the optimization constraint of chilled water component block 90 via the optimization view 132 of graphical user interface 80.For example, Figure 10 is the instance of the graphical user interface 80 (, diagrammatic representation) of the graphical modeling instrument 82 of the optimization view 132 of illustration user 106 when having submitted order input (that is, revised and optimized constraint) to and having upgraded the optimal solution of system 10.More specifically, in the instance of in Figure 10, describing, user 106 has revised the time series 134 of the expectation chilled water demand of chilled water component block 90, and has upgraded the time schedule 136 of four refrigerator component blocks 88.Especially, the model modification of control system 96 optimization problem of system 10, to confirm and should turn-off refrigerator component block EC.0 between 16:00 and the 18:00 and should between 16:00 and 18:00, open refrigerator component block EC.2.Illustrative like institute among Figure 10, present to user 106 bottom of graphical user interface 80 (for example) to the cost of submitting modification.In a particular embodiment, introduce the cost of optimizing constraint and can be reported to all users 106, and write down (for example, in the database in for example residing in control/optimization system 96) with appropriate format.Can carry out this type of optimizing constraint to any component block in the component block (that is, the parameter mixture model 58) of the network 84 that shown by graphical user interface 80 revises.Because the global optimization strategy in the control/optimization system 96, calculate and to the user cost that retrains in accordance with user's 106 redeterminations is shown immediately as shown in Figure 10.In order to see immediately also that through graphics mode varying loading profile (profile) (for example, time series 134) ability of cost/saving under the various load profiles is the unique ability that realizes through the graphic language that is used to optimize that appears among this paper.
Component block is a parameter mixture model 58, and so, usually and nonlinear model (even being under the situation of degeneration (degenerate) form of parameter mixture model 58 at linear model).Therefore, the network 84 that comprises parameter mixture model 58 does not really want to become the linear optimization problem similarly.Correspondingly, when user 106 revises the optimization constraint of parameter mixture model 58, definite some complicacy of the optimization problem that right periodical repair changes.The method for optimizing that is used for confirming the modification optimization problem of graphics-optimized language is to use protruding the approaching of data-driven on the track of network model 84 each parameter mixture model 58.Through definition, function f is being protruding as follows in this case:
f(λx+(1-λ)y)≤λf(x)+(1-λ)f(y)，
In addition, if f and g are convex functions, so then be:
αf+βg，
So the overall model of expression network model 84 will be protruding.Any local minimum of convex function also is a global minimum.Non-protruding optimization problem benefits from compact, protruding estimation down.Suppose that f is a twice differentiable function, then f is being protruding as follows in this case and only as follows in this case:
In the diagrammatic representation of optimization problem (for example, the network model 84 shown in Fig. 8), each node 92 exposes the decision variable of optimization problem.Each connects 94 and confirms decision variable in two nodes 92 how to be correlated with (for example, restrained).Therefore, diagrammatic representation has directly translating to the optimization problem narration.Can through linear matrix operation catch network topology and via with the graphical interaction of network 84 (for example, increase node 92, remove connect 94) to any modification of network topology.Therefore, will decide optimization problem utilizing convex function to approach under the situation of each parts in the network 84 and be translated into the diagrammatic representation of optimization problem to fit.The method for optimizing of this convexification is to use network components to drive protruding approaching along the automaticdata of predicted operation track in the graphic language disclosed herein.Parameter hybrid modeling example allows to have this protruding approaching of expected degree accuracy.Therefore, can use the protruding system's of the solving 10 Model Optimization problems of approaching that to carry out the continuous convexification of feasible region through the iteration that is confined to feasible region.For example, Figure 11 is the instance of two variablees 140,142 non-linear and non-convex function 138 relative to each other.Like institute's example, two protruding approaches 144,146 provides under different protruding of accuracy and approaches symbol (underapproximator).
In addition, in a particular embodiment, do not be sure of solution to optimization problem 138 with the determinacy mode.In other words, not to be independent of the point that begins to confirm to confirm optimal solution.But, can confirm optimal solution through considering previous optimal solution.For example; Get back to the modification instance of the optimization constraint of describing to Figure 10, at this moment between section manipulate refrigerator component block EC.0, refrigerator component block EC.1 and refrigerator component block EC.3 be the renewal that begins optimal solution between 16:00 and the 18:00 under the hypothesis of optimal solution.So, the optimal solution of being revised only changes scheduling times 136, make at this moment between section manipulate refrigerator component block EC.2 but not refrigerator component block EC.0.In other words, model is attempted to press close to as far as possible previous optimal solution ground (that is, with the uncertainty mode) and is reached optimal solution.
As an example, can be through following function definition scheduling problem formula:
Minimize
Make
f+Hp-g＝0
Zp≥δ
μ
iy
i≤p
i≤ξ
iy
i
y
i∈{0，1}
Wherein, M is the set of unit operations, and N is the set of input, and β, σ and κ are respectively the costs that is associated with the purchase of the sale of the import of product, product and resource; R is given resource input; P is the product that the discrete cell operation generates, and A and B are the subclass of unit operations model Γ restriction for input and product, and
is the set of the fitting parameter of given model; μ and ξ are the unit operations borders; H, f and g allow product import and outlet, and Z and δ are provided with the demand needs, and y is the two-valued variable of location mode.User 106 can define linear network model constraint H and Z (for example, through clicking parameter mixture model 58 via graphical user interface 80).In addition, user 106 can also define discrete (or two-value) decision variable y
iIn addition, user 106 can also define constrained parameters δ, f, g, β, σ and κ.
Figure 12 is the instance of solution Figure 148 of above-mentioned optimal solution equality.Can solution Figure 148 be called bearing tree D=(V, E), wherein, V be unit operations model, product and resource V=(Γ, p, r, f, set g), E connects E=(H, set Z).Usually, the set of unit operations model V and parameter mixture model 58 (that is, the node 92 of network model 84) are similar, and being connected of E and network model 84 is 94 similar.Figure 12 is clear to have proved that the well-posedness of optimization problem is not inappreciable through non-linear unit operation model Γ, and the graphical manipulation of solution figure is not easily manageable.It is can be with the optimization approach of this solution figure of graphics mode management in order to draw that continuous data drives convexification.
Figure 13 is the instance that is used for using the mutual method 150 of parameter mixture model that graphical user interface 80 and this paper describes 58.In step 152, can confirm during to remote electronic device 110 typing login certificate user 106, or can use other method (such as access rights etc.) to confirm user 106 access level with storage on the electronic installation 110.For example, as stated, when user 106 signed in in the electronic installation 110, graphical modeling instrument 82 can confirm that user 106 is supvr's rank user 120 or slip-stick artist's rank user 122.Yet, can use and can realize authorizing and functional meticulousr other other access level of level.
In step 154, make 80 pairs of electronic installations 110 of graphical user interface to use from the graphical modeling instrument 82 of control/optimization system 96.Graphical user interface 80 realize relevant with parameter mixture model 58 (that is, relevant parameter mixture model) with the actual physics parts of factory and/or enterprise and with the corresponding a plurality of orders inputs of user 106 access level.For example, suppose that user 106 has the suitable access rights to special parameter mixture model 58, then can realize being used to revise the order input of the optimization constraint (for example, prediction load profile) of parameter mixture model 58 via graphical user interface 80.In addition; Suppose that once more user 106 has the suitable access rights to special parameter mixture model 58; Then can realize being used to revise the order input how parameter mixture model 58 operates (for example, the input of parameter mixture model 58, output, parameter etc.) via graphical user interface 80.
In addition; As in greater detail above; Graphical user interface 80 realizes the demonstration as a plurality of parameter mixture models 58 of node 92 expressions of prototype network 84, and a plurality of input and output that are represented as a plurality of parameter mixture models 58 of the connection 94 between the node 92 of prototype network 84.Graphical user interface 80 make user 106 can from prototype network 84 increase or deletion of node 92 with is connected 94, carry out the personalization demonstration of mutual parameter mixture model 58 with establishment authorized user 106.
In step 156, the graphical modeling instrument 82 of control/optimization system 96 receives the order input from graphical user interface 80.As stated, in a particular embodiment, can send the order input (that is broadcasting) via other electronic installation 110 and give other user 106 of control system 96.Then, in step 158, graphical modeling instrument 82 is according to the user's 106 who submits the order input access level processing command input.For example, in a particular embodiment, can confirm in the parameter mixture model 58 one or the model quality of multiparameter mixture model 58 more in the operating period of system 10.As stated, in order to inquiry system 10 operating period model quality ability be because the transparent person's character of graphical modeling instrument 82.In addition, in a particular embodiment, have in order to the mandate of making this request and ask under the feasible situation supposition user 106, control system 96 can be readjusted the optimization problem of prototype network 84 automatically in the operating period of system 10.Yet in a particular embodiment, order input can also be before being carried out by control system 96, the permission of accepting to be undertaken by slip-stick artist's rank user 122, accepts boundary constraint (for example, can only allow in the particular range change) etc.
In any case, can use all order inputs, to revise the control of system 10 in the operating period of system 10 via control/optimization system 96.For example; Use the above instance of describing to Figure 10, if the optimization of one of user's modification parameter mixture model 58 constraint, and would find to revise through control system 96 feasible (for example; Via graphical modeling instrument 82), then control/optimization system 96 can be implemented the gained optimal solution automatically.For example, can be according to the actuator 100 of the parts 102 of revising optimal solution actuating system 10.Again, use the instance of describing to Figure 10, control system 96 can automatic control system 10, between 16:00 and 18:00, turn-offing refrigerator component block EC.0, and between 16:00 and 18:00, starts refrigerator component block EC.2.
Though in this article an example with some characteristic of the present invention has been described, many modifications and change will appear to those skilled in the art.Therefore should be appreciated that accompanying claims is intended to as in dropping on true spirit of the present invention, cover all this modification and changes.
About comprising the embodiment of above each embodiment, following remarks is also disclosed:
Remarks:
1. the parameter mixture model system controller/optimizer that enterprise integrates comprises the non-provisional computer-readable medium of computer instruction being encoded above that, and wherein, said computer instruction comprises instruction, is used for:
To remote electronic device transmission of graphical user interface; Wherein, Said graphical user interface realizes and retrains a plurality of orders relevant with target with the relevant parameter mixture model of the physical unit of the factory of said enterprise and control/optimizations and import; And wherein, the order of said realization input is corresponding to the user's of the said electronic installation of operation access level;
Reception is from one or more order input of said graphical user interface; And
According to said user's access level, handle said one or more order input.
2. like remarks 1 described system controller/optimizer, wherein, handle the operation that said one or more order input comprises control and/or optimizes said factory.
3. like remarks 1 described system controller/optimizer, wherein, handle the model quality that operating period that said one or more order input is included in said factory confirms said parameter mixture model.
4. like remarks 1 described system controller/optimizer, wherein, said computer instruction comprises and being used for the instruction of the information transmission relevant with said one or more order input that receives from said user to other remote electronic device.
5. like remarks 1 described system controller/optimizer; Wherein, Receive said one or more order input and comprise reception one or more order input, and handle said one or more and order input to comprise one or more parameter mixture model of revising in the said parameter mixture model from one or more slip-stick artist's rank user of said enterprise.
6. like remarks 5 described system controller/optimizers; Wherein, Based on authority levels protection said one or more order input from said one or more slip-stick artist's rank user, and wherein, the influence of said one or more order input is visible to all users.
7. like remarks 1 described system controller/optimizer; Wherein, Receive said one or more order input and comprise reception one or more order input, and handle said one or more and order input to comprise optimization constraint or the target of revising said enterprise from one or more supvr's rank user of said enterprise.
8. like remarks 7 described system controller/optimizers; Wherein, Based on authority levels protection said one or more order input from said one or more supvr's rank user, and wherein, the influence of said one or more order input is visible to all users.
9. like remarks 1 described system controller/optimizer, wherein, said computer instruction comprises the instruction that is used for receiving from another proper authorization user the modification permission of the said parameter mixture model that the user is submitted.
10. like remarks 1 described system controller/optimizer, wherein, said computer instruction comprises parameter mixture model or the optimization constraint of modification and/or the instruction that target revises automatically the optimal solution of said enterprise that is used for based on the modification of said enterprise.
11. a method comprises:
To remote electronic device transmission of graphical user interface; Wherein, Said graphical user interface realizes and imports with the relevant a plurality of orders of the relevant parameter mixture model of the physical unit of factory, and wherein, the order of said realization input is corresponding to the user's of the said electronic installation of operation access level;
Reception is from the order input of said graphical user interface; And
According to said user's access level, handle said order input.
12., wherein, handle said order input and comprise the operation of optimizing and/or controlling said factory like remarks 11 described methods.
13., wherein, handle the model quality that operating period that said order input is included in said factory confirms said parameter mixture model like remarks 11 described methods.
14., comprise with giving other remote electronic device with the relevant information transmission of said order input that receives from said user like remarks 11 described methods.
15. like remarks 11 described methods; Wherein, Receive said order input and comprise the order input of reception, and handle said order input and comprise one or more parameter mixture model of revising in the said parameter mixture model from slip-stick artist's rank user of said factory.
16. like remarks 11 described methods, wherein, receive said order input and comprise the order input of reception, and handle said order input and comprise that the optimization of revising said factory retrains from supvr's rank user of said enterprise.
17., comprise the modification permission that receives the said parameter mixture model that the user is submitted from another proper authorization user like remarks 11 described methods.
18., comprise the optimal solution that the optimization constraint based on the parameter mixture model of the modification of said factory or modification revises automatically said factory like remarks 11 described methods.
19., be included in said user confirms access level from said user to said electronic installation typing login certificate like remarks 11 described methods.
20. a non-provisional computer-readable medium of above that computer instruction being encoded, wherein, computer instruction comprises instruction, is used for:
Confirm the user's of operating electronic devices access level;
To said electronic installation transmission of graphical user interface; Wherein, Said graphical user interface realizes and imports with the relevant a plurality of orders of the relevant parameter mixture model of the physical unit of factory; And wherein, the order of said realization input is corresponding to the said user's of the said electronic installation of operation access level;
Reception is from the order input of said graphical user interface;
According to said user's access level, handle said order input, wherein, handle the model quality that operating period that said order input is included in said factory confirms said parameter mixture model; And
With giving other remote electronic device with the relevant information transmission of said order input that receives from said user.
Claims (10)
1. the parameter mixture model system controller/optimizer that enterprise integrates comprises the non-provisional computer-readable medium of computer instruction being encoded above that, and wherein, said computer instruction comprises instruction, is used for:
To remote electronic device transmission of graphical user interface; Wherein, Said graphical user interface realizes and retrains a plurality of orders relevant with target with the relevant parameter mixture model of the physical unit of the factory of said enterprise and control/optimizations and import; And wherein, the order of said realization input is corresponding to the user's of the said electronic installation of operation access level;
Reception is from one or more order input of said graphical user interface; And
According to said user's access level, handle said one or more order input.
2. the system of claim 1 controller/optimizer wherein, is handled the operation that said one or more order input comprises control and/or optimizes said factory.
3. the system of claim 1 controller/optimizer wherein, is handled the model quality that operating period that said one or more order input is included in said factory confirms said parameter mixture model.
4. the system of claim 1 controller/optimizer, wherein, said computer instruction comprises the instruction that is used for giving other remote electronic device with the relevant information transmission of said one or more order input that receives from said user.
5. the system of claim 1 controller/optimizer; Wherein, Receive said one or more order input and comprise reception one or more order input, and handle said one or more and order input to comprise one or more parameter mixture model of revising in the said parameter mixture model from one or more slip-stick artist's rank user of said enterprise.
6. the system of claim 1 controller/optimizer; Wherein, Receive said one or more order input and comprise reception one or more order input, and handle said one or more and order input to comprise optimization constraint or the target of revising said enterprise from one or more supvr's rank user of said enterprise.
7. the system of claim 1 controller/optimizer, wherein, said computer instruction comprises the instruction that is used for receiving from another proper authorization user the modification permission of the said parameter mixture model that the user is submitted.
8. the system of claim 1 controller/optimizer; Wherein, said computer instruction comprises parameter mixture model or the optimization constraint of modification and/or the instruction that target revises automatically the optimal solution of said enterprise that is used for based on the modification of said enterprise.
9. method comprises:
To remote electronic device transmission of graphical user interface; Wherein, Said graphical user interface realizes and imports with the relevant a plurality of orders of the relevant parameter mixture model of the physical unit of factory, and wherein, the order of said realization input is corresponding to the user's of the said electronic installation of operation access level;
Reception is from the order input of said graphical user interface; And
According to said user's access level, handle said order input.
10. method as claimed in claim 9 is included in said user confirms access level from said user to said electronic installation typing login certificate.
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Also Published As
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| EP2500787B1 (en) | 2016-08-17 |
| EP2500787A1 (en) | 2012-09-19 |
| US20120239169A1 (en) | 2012-09-20 |
| CN102707621B (en) | 2016-08-10 |
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